On the accuracy of dynamic mode decomposition in estimating instability of wave packet
Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pan, Chong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
Proper Orthogonal Decomposition |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Experiments in fluids - Berlin : Springer, 1983, 56(2015), 8 vom: 04. Aug. |
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| Übergeordnetes Werk: |
volume:56 ; year:2015 ; number:8 ; day:04 ; month:08 |
| Links: |
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| DOI / URN: |
10.1007/s00348-015-2015-6 |
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| Katalog-ID: |
SPR004383990 |
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| 100 | 1 | |a Pan, Chong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a On the accuracy of dynamic mode decomposition in estimating instability of wave packet |
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| 520 | |a Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/spatial growth rate. Therefore, it is necessary to evaluate the accuracy performance and confidence limit of DMD algorithm in the circumstance of multi-scale instability wave packet. In the present study, we use a linear combination of a sinusoidal unstable wave and its high-order harmonics as a prototype, based on which an error analysis of DMD algorithm is taken. In first, different numerical algorithms of DMD analysis are compared in terms of both accuracy and efficiency. The accuracy evaluation of the classical DMD algorithm in a large parameter domain is followed. It is found that the superimposition of finer structures with less energy dominance might damage the estimation accuracy of the primary structures’ growth rate. Strong evidences suggest that even in a linear circumstance, resolving the dynamics of small-scale structures is comparably more difficult than that of the primary structures, i.e., DMD algorithm has a preference for structures with energetic dominance. Finally, the recommended thresholds for the sampling/discretizing parameters are summarized for practical usage. | ||
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| 700 | 1 | |a Xue, Dong |4 aut | |
| 700 | 1 | |a Wang, Jinjun |4 aut | |
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10.1007/s00348-015-2015-6 doi (DE-627)SPR004383990 (SPR)s00348-015-2015-6-e DE-627 ger DE-627 rakwb eng Pan, Chong verfasserin aut On the accuracy of dynamic mode decomposition in estimating instability of wave packet 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/spatial growth rate. Therefore, it is necessary to evaluate the accuracy performance and confidence limit of DMD algorithm in the circumstance of multi-scale instability wave packet. In the present study, we use a linear combination of a sinusoidal unstable wave and its high-order harmonics as a prototype, based on which an error analysis of DMD algorithm is taken. In first, different numerical algorithms of DMD analysis are compared in terms of both accuracy and efficiency. The accuracy evaluation of the classical DMD algorithm in a large parameter domain is followed. It is found that the superimposition of finer structures with less energy dominance might damage the estimation accuracy of the primary structures’ growth rate. Strong evidences suggest that even in a linear circumstance, resolving the dynamics of small-scale structures is comparably more difficult than that of the primary structures, i.e., DMD algorithm has a preference for structures with energetic dominance. Finally, the recommended thresholds for the sampling/discretizing parameters are summarized for practical usage. Wave Packet (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Basis Wave (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Xue, Dong aut Wang, Jinjun aut Enthalten in Experiments in fluids Berlin : Springer, 1983 56(2015), 8 vom: 04. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:56 year:2015 number:8 day:04 month:08 https://dx.doi.org/10.1007/s00348-015-2015-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2015 8 04 08 |
| spelling |
10.1007/s00348-015-2015-6 doi (DE-627)SPR004383990 (SPR)s00348-015-2015-6-e DE-627 ger DE-627 rakwb eng Pan, Chong verfasserin aut On the accuracy of dynamic mode decomposition in estimating instability of wave packet 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/spatial growth rate. Therefore, it is necessary to evaluate the accuracy performance and confidence limit of DMD algorithm in the circumstance of multi-scale instability wave packet. In the present study, we use a linear combination of a sinusoidal unstable wave and its high-order harmonics as a prototype, based on which an error analysis of DMD algorithm is taken. In first, different numerical algorithms of DMD analysis are compared in terms of both accuracy and efficiency. The accuracy evaluation of the classical DMD algorithm in a large parameter domain is followed. It is found that the superimposition of finer structures with less energy dominance might damage the estimation accuracy of the primary structures’ growth rate. Strong evidences suggest that even in a linear circumstance, resolving the dynamics of small-scale structures is comparably more difficult than that of the primary structures, i.e., DMD algorithm has a preference for structures with energetic dominance. Finally, the recommended thresholds for the sampling/discretizing parameters are summarized for practical usage. Wave Packet (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Basis Wave (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Xue, Dong aut Wang, Jinjun aut Enthalten in Experiments in fluids Berlin : Springer, 1983 56(2015), 8 vom: 04. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:56 year:2015 number:8 day:04 month:08 https://dx.doi.org/10.1007/s00348-015-2015-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2015 8 04 08 |
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10.1007/s00348-015-2015-6 doi (DE-627)SPR004383990 (SPR)s00348-015-2015-6-e DE-627 ger DE-627 rakwb eng Pan, Chong verfasserin aut On the accuracy of dynamic mode decomposition in estimating instability of wave packet 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/spatial growth rate. Therefore, it is necessary to evaluate the accuracy performance and confidence limit of DMD algorithm in the circumstance of multi-scale instability wave packet. In the present study, we use a linear combination of a sinusoidal unstable wave and its high-order harmonics as a prototype, based on which an error analysis of DMD algorithm is taken. In first, different numerical algorithms of DMD analysis are compared in terms of both accuracy and efficiency. The accuracy evaluation of the classical DMD algorithm in a large parameter domain is followed. It is found that the superimposition of finer structures with less energy dominance might damage the estimation accuracy of the primary structures’ growth rate. Strong evidences suggest that even in a linear circumstance, resolving the dynamics of small-scale structures is comparably more difficult than that of the primary structures, i.e., DMD algorithm has a preference for structures with energetic dominance. Finally, the recommended thresholds for the sampling/discretizing parameters are summarized for practical usage. Wave Packet (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Basis Wave (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Xue, Dong aut Wang, Jinjun aut Enthalten in Experiments in fluids Berlin : Springer, 1983 56(2015), 8 vom: 04. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:56 year:2015 number:8 day:04 month:08 https://dx.doi.org/10.1007/s00348-015-2015-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2015 8 04 08 |
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10.1007/s00348-015-2015-6 doi (DE-627)SPR004383990 (SPR)s00348-015-2015-6-e DE-627 ger DE-627 rakwb eng Pan, Chong verfasserin aut On the accuracy of dynamic mode decomposition in estimating instability of wave packet 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/spatial growth rate. Therefore, it is necessary to evaluate the accuracy performance and confidence limit of DMD algorithm in the circumstance of multi-scale instability wave packet. In the present study, we use a linear combination of a sinusoidal unstable wave and its high-order harmonics as a prototype, based on which an error analysis of DMD algorithm is taken. In first, different numerical algorithms of DMD analysis are compared in terms of both accuracy and efficiency. The accuracy evaluation of the classical DMD algorithm in a large parameter domain is followed. It is found that the superimposition of finer structures with less energy dominance might damage the estimation accuracy of the primary structures’ growth rate. Strong evidences suggest that even in a linear circumstance, resolving the dynamics of small-scale structures is comparably more difficult than that of the primary structures, i.e., DMD algorithm has a preference for structures with energetic dominance. Finally, the recommended thresholds for the sampling/discretizing parameters are summarized for practical usage. Wave Packet (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Basis Wave (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Xue, Dong aut Wang, Jinjun aut Enthalten in Experiments in fluids Berlin : Springer, 1983 56(2015), 8 vom: 04. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:56 year:2015 number:8 day:04 month:08 https://dx.doi.org/10.1007/s00348-015-2015-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2015 8 04 08 |
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10.1007/s00348-015-2015-6 doi (DE-627)SPR004383990 (SPR)s00348-015-2015-6-e DE-627 ger DE-627 rakwb eng Pan, Chong verfasserin aut On the accuracy of dynamic mode decomposition in estimating instability of wave packet 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2015 Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/spatial growth rate. Therefore, it is necessary to evaluate the accuracy performance and confidence limit of DMD algorithm in the circumstance of multi-scale instability wave packet. In the present study, we use a linear combination of a sinusoidal unstable wave and its high-order harmonics as a prototype, based on which an error analysis of DMD algorithm is taken. In first, different numerical algorithms of DMD analysis are compared in terms of both accuracy and efficiency. The accuracy evaluation of the classical DMD algorithm in a large parameter domain is followed. It is found that the superimposition of finer structures with less energy dominance might damage the estimation accuracy of the primary structures’ growth rate. Strong evidences suggest that even in a linear circumstance, resolving the dynamics of small-scale structures is comparably more difficult than that of the primary structures, i.e., DMD algorithm has a preference for structures with energetic dominance. Finally, the recommended thresholds for the sampling/discretizing parameters are summarized for practical usage. Wave Packet (dpeaa)DE-He213 Proper Orthogonal Decomposition (dpeaa)DE-He213 Basis Wave (dpeaa)DE-He213 Proper Orthogonal Decomposition Mode (dpeaa)DE-He213 Dynamic Mode Decomposition (dpeaa)DE-He213 Xue, Dong aut Wang, Jinjun aut Enthalten in Experiments in fluids Berlin : Springer, 1983 56(2015), 8 vom: 04. Aug. (DE-627)270126295 (DE-600)1476361-8 1432-1114 nnns volume:56 year:2015 number:8 day:04 month:08 https://dx.doi.org/10.1007/s00348-015-2015-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2015 8 04 08 |
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Pan, Chong @@aut@@ Xue, Dong @@aut@@ Wang, Jinjun @@aut@@ |
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On the accuracy of dynamic mode decomposition in estimating instability of wave packet |
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on the accuracy of dynamic mode decomposition in estimating instability of wave packet |
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On the accuracy of dynamic mode decomposition in estimating instability of wave packet |
| abstract |
Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/spatial growth rate. Therefore, it is necessary to evaluate the accuracy performance and confidence limit of DMD algorithm in the circumstance of multi-scale instability wave packet. In the present study, we use a linear combination of a sinusoidal unstable wave and its high-order harmonics as a prototype, based on which an error analysis of DMD algorithm is taken. In first, different numerical algorithms of DMD analysis are compared in terms of both accuracy and efficiency. The accuracy evaluation of the classical DMD algorithm in a large parameter domain is followed. It is found that the superimposition of finer structures with less energy dominance might damage the estimation accuracy of the primary structures’ growth rate. Strong evidences suggest that even in a linear circumstance, resolving the dynamics of small-scale structures is comparably more difficult than that of the primary structures, i.e., DMD algorithm has a preference for structures with energetic dominance. Finally, the recommended thresholds for the sampling/discretizing parameters are summarized for practical usage. © Springer-Verlag Berlin Heidelberg 2015 |
| abstractGer |
Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/spatial growth rate. Therefore, it is necessary to evaluate the accuracy performance and confidence limit of DMD algorithm in the circumstance of multi-scale instability wave packet. In the present study, we use a linear combination of a sinusoidal unstable wave and its high-order harmonics as a prototype, based on which an error analysis of DMD algorithm is taken. In first, different numerical algorithms of DMD analysis are compared in terms of both accuracy and efficiency. The accuracy evaluation of the classical DMD algorithm in a large parameter domain is followed. It is found that the superimposition of finer structures with less energy dominance might damage the estimation accuracy of the primary structures’ growth rate. Strong evidences suggest that even in a linear circumstance, resolving the dynamics of small-scale structures is comparably more difficult than that of the primary structures, i.e., DMD algorithm has a preference for structures with energetic dominance. Finally, the recommended thresholds for the sampling/discretizing parameters are summarized for practical usage. © Springer-Verlag Berlin Heidelberg 2015 |
| abstract_unstemmed |
Abstract Lots of unstable flows in both nature and engineering pose multi-scale perturbations with infinitesimal initial amplitude, which compete and interact with each other during their unstable evolution. Dynamic mode decomposition (DMD) analysis can be used to extract these components’ temporal/spatial growth rate. Therefore, it is necessary to evaluate the accuracy performance and confidence limit of DMD algorithm in the circumstance of multi-scale instability wave packet. In the present study, we use a linear combination of a sinusoidal unstable wave and its high-order harmonics as a prototype, based on which an error analysis of DMD algorithm is taken. In first, different numerical algorithms of DMD analysis are compared in terms of both accuracy and efficiency. The accuracy evaluation of the classical DMD algorithm in a large parameter domain is followed. It is found that the superimposition of finer structures with less energy dominance might damage the estimation accuracy of the primary structures’ growth rate. Strong evidences suggest that even in a linear circumstance, resolving the dynamics of small-scale structures is comparably more difficult than that of the primary structures, i.e., DMD algorithm has a preference for structures with energetic dominance. Finally, the recommended thresholds for the sampling/discretizing parameters are summarized for practical usage. © Springer-Verlag Berlin Heidelberg 2015 |
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On the accuracy of dynamic mode decomposition in estimating instability of wave packet |
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Xue, Dong Wang, Jinjun |
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|
| score |
7.400401 |